Applied statistics and probability: Random self-similar trees and networks; coalescent and branching processes; multiscale methods of time series analysis; random sums of heavy-tailed variables; delay dynamical systems. Applications: statistical seismology; river networks and delta systems; climate stability; El-Niño modeling; stochastic dynamics of intracellular protein motors; financial stochastic modeling.

• Kovchegov, Y. and I. Zaliapin (2020) Random self-similar trees: A mathematical theory of Horton laws. Probability Surveys, 17, 1-213. doi:10.1214/19-PS331


• Zaliapin, I. and Y. Ben-Zion (2020) Earthquake declustering using the nearest‐neighbor approach in space‐time‐magnitude domain. J. Geophys. Res.: Solid Earth, e53991, Supplementary Materials, Example: declustered catalog of SoCal and Catalog format, doi:10.1029/2018JB017120


• Tejedor, A., A. Longjas, I. Zaliapin, and E. Foufoula-Georgiou (2015) Delta channel networks: 1. A graph-theoretic approach for studying connectivity and steady state transport on deltaic surfaces. Water Resources Research, 51, 3998–4018. doi: 10.1002/2014WR016577,


• Zaliapin, I. and Y. Ben-Zion (2013) Earthquake clusters in southern California, I: Identification and stability. J. Geophys. Res., 118(6), 2847-2864, Supplementary Materials, doi:10.1002/jgrb.50179


• Zaliapin, I., A. Gabrielov, V. Keilis-Borok, H. Wong (2008) Clustering analysis of seismicity and aftershock identification, Physical Review Letters, 101(1), 018501. doi:10.1103/PhysRevLett.101.018501


• Zaliapin, I., Y. Kagan, and F. Schoenberg (2005) Approximating the distribution of Pareto sums, Pure and Applied Geophysics, 162(6-7), 1187-1228. doi:10.1007/s00024-004-2666-3
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